one noise variable, logistic regression
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## [1] "one noise variable, logistic regression"
## [1] "bSigmaBest 39"
## [1] "naive effects model"
## [1] "one noise variable, logistic regression naive effects model fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.8068 -1.0493 0.5770 0.9415 2.5190
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.18447 0.05074 3.635 0.000277 ***
## n1 2.20269 0.13545 16.262 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2772.6 on 1999 degrees of freedom
## Residual deviance: 2256.7 on 1998 degrees of freedom
## AIC: 2260.7
##
## Number of Fisher Scoring iterations: 6
##
## [1] "one noise variable, logistic regression naive effects model train mean deviance 1.62786601580457"
## [1] "one noise variable, logistic regression naive effects model test mean deviance 3.71500787962648"
## [1] "effects model, sigma= 39"
## [1] "one noise variable, logistic regression effects model, sigma= 39 fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.246 -1.195 1.109 1.151 1.409
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.03916 0.04623 0.847 0.39699
## n1 0.18610 0.05755 3.233 0.00122 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2772.6 on 1999 degrees of freedom
## Residual deviance: 2762.0 on 1998 degrees of freedom
## AIC: 2766
##
## Number of Fisher Scoring iterations: 3
##
## [1] "one noise variable, logistic regression Noised 39 train mean deviance 1.99233779252217"
## [1] "one noise variable, logistic regression Noised 39 test mean deviance 2.00622149816528"
## [1] "effects model, jacknifed"
## [1] "one noise variable, logistic regression effects model, jackknifed fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -1.3619 -1.1570 0.9662 1.1980 1.2169
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.04838 0.04731 -1.023 0.30650
## n1 -0.06366 0.01954 -3.258 0.00112 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2772.6 on 1999 degrees of freedom
## Residual deviance: 2761.8 on 1998 degrees of freedom
## AIC: 2765.8
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one noise variable, logistic regression jackknifed train mean deviance 1.99219567357296"
## [1] "one noise variable, logistic regression jackknifed test mean deviance 2.00542702505421"
## [1] "********"
## [1] "one noise variable, logistic regression AverageManyNoisedModels"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.999 2.000 2.001 2.001 2.001 2.004
## [1] 0.0008298006
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.998 2.000 2.002 2.003 2.005 2.013
## [1] 0.002991706
## [1] "********"
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## [1] "one noise variable, logistic regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 3.500 3.882 3.973 3.994 4.151 4.452
## [1] 0.1903538
## [1] "********"
## [1] "********"
## [1] "one noise variable, logistic regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.000 2.002 2.004 2.005 2.007 2.015
## [1] 0.003271046
## [1] "********"
## [1] "*************************************************************"
one variable, logistic regression
## [1] "*************************************************************"
## [1] "one variable, logistic regression"
## [1] "bSigmaBest 10"
## [1] "naive effects model"
## [1] "one variable, logistic regression naive effects model fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.1243 -1.1809 0.4704 1.1554 1.5778
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.4731 0.0542 8.73 <2e-16 ***
## x1 3.1777 0.2114 15.03 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2434.7 on 1998 degrees of freedom
## AIC: 2438.7
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one variable, logistic regression naive effects model train mean deviance 1.75629049009229"
## [1] "one variable, logistic regression naive effects model test mean deviance 1.74484448505444"
## [1] "effects model, sigma= 10"
## [1] "one variable, logistic regression effects model, sigma= 10 fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0258 -1.1625 0.5245 1.1525 1.6410
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.41361 0.05161 8.014 1.11e-15 ***
## x1 3.04056 0.20227 15.032 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2456.2 on 1998 degrees of freedom
## AIC: 2460.2
##
## Number of Fisher Scoring iterations: 3
##
## [1] "one variable, logistic regression Noised 10 train mean deviance 1.7717663587731"
## [1] "one variable, logistic regression Noised 10 test mean deviance 1.76627324921548"
## [1] "effects model, jacknifed"
## [1] "one variable, logistic regression effects model, jackknifed fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.0811 -1.1892 0.4966 1.1600 1.5642
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.45308 0.05326 8.508 <2e-16 ***
## x1 2.99703 0.20478 14.636 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2460.2 on 1998 degrees of freedom
## AIC: 2464.2
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one variable, logistic regression jackknifed train mean deviance 1.77463669725858"
## [1] "one variable, logistic regression jackknifed test mean deviance 1.746225629925"
## [1] "********"
## [1] "one variable, logistic regression AverageManyNoisedModels"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.740 1.762 1.769 1.769 1.778 1.796
## [1] 0.01251551
## [1] "********"
## [1] "********"
## [1] "one variable, logistic regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.741 1.763 1.770 1.770 1.780 1.796
## [1] 0.01237005
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## [1] "one variable, logistic regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.739 1.763 1.770 1.771 1.781 1.798
## [1] 0.01305661
## [1] "********"
## [1] "********"
## [1] "one variable, logistic regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.738 1.763 1.772 1.773 1.783 1.834
## [1] 0.01674702
## [1] "********"
## [1] "*************************************************************"
one variable plus noise variable, logistic regression
## [1] "*************************************************************"
## [1] "one variable plus noise variable, logistic regression"
## [1] "bSigmaBest 18"
## [1] "naive effects model"
## [1] "one variable plus noise variable, logistic regression naive effects model fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.5658 -0.9120 0.3055 0.8035 2.7112
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.68760 0.06161 11.16 <2e-16 ***
## x1 3.18452 0.23641 13.47 <2e-16 ***
## n1 2.45247 0.15572 15.75 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 1990.5 on 1997 degrees of freedom
## AIC: 1996.5
##
## Number of Fisher Scoring iterations: 6
##
## [1] "one variable plus noise variable, logistic regression naive effects model train mean deviance 1.43587337720022"
## [1] "one variable plus noise variable, logistic regression naive effects model test mean deviance 3.54303901440774"
## [1] "effects model, sigma= 18"
## [1] "one variable plus noise variable, logistic regression effects model, sigma= 18 fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.111 -1.140 0.505 1.110 1.748
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.50496 0.05486 9.205 < 2e-16 ***
## x1 3.24797 0.21623 15.021 < 2e-16 ***
## n1 0.25764 0.07576 3.401 0.000672 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2445.4 on 1997 degrees of freedom
## AIC: 2451.4
##
## Number of Fisher Scoring iterations: 3
##
## [1] "one variable plus noise variable, logistic regression Noised 18 train mean deviance 1.76398528021888"
## [1] "one variable plus noise variable, logistic regression Noised 18 test mean deviance 1.78996190188785"
## [1] "effects model, jacknifed"
## [1] "one variable plus noise variable, logistic regression effects model, jackknifed fit model:"
##
## Call:
## glm(formula = formulaL, family = binomial, data = trainData)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2012 -1.1757 0.5026 1.1657 1.5936
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.42346 0.05493 7.710 1.26e-14 ***
## x1 3.00699 0.20534 14.644 < 2e-16 ***
## n1 -0.05278 0.02435 -2.167 0.0302 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2747.0 on 1999 degrees of freedom
## Residual deviance: 2455.4 on 1997 degrees of freedom
## AIC: 2461.4
##
## Number of Fisher Scoring iterations: 4
##
## [1] "one variable plus noise variable, logistic regression jackknifed train mean deviance 1.77119992923416"
## [1] "one variable plus noise variable, logistic regression jackknifed test mean deviance 1.77521675815884"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression AverageManyNoisedModels"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.742 1.760 1.771 1.772 1.782 1.803
## [1] 0.01360162
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression JackknifeModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.733 1.762 1.769 1.770 1.778 1.806
## [1] 0.01417061
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression NaiveModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.875 3.429 3.539 3.567 3.703 4.151
## [1] 0.2140362
## [1] "********"
## [1] "********"
## [1] "one variable plus noise variable, logistic regression NoisedModel"
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.738 1.787 1.800 1.804 1.821 1.876
## [1] 0.02458687
## [1] "********"
## [1] "*************************************************************"